1. Field of the Invention
The present invention relates to a unit for automatically tuning control parameters used in an automatic control unit, and to a method of tuning the same. More specifically, the present invention relates to a unit and method for optimizing fuzzy knowledge used in a fuzzy control unit. The present invention also relates to a unit and a method for optimizing PID parameters of a PID (Proportional, Integral, Differential) control unit.
2. Description of the Background Art
Various automatic control units have been used for automatically setting plants under control to desired states. A control unit utilizing a method of control called fuzzy control has been known as one of such control units.
In fuzzy control, a plant is controlled by finding an optimal manipulated variable by an arithmetic operation (fuzzy inference) for the plant under control, from, for example, control (response) deviation and difference (differential) information thereof, based on fuzzy knowledge including membership functions and fuzzy control rules.
Since a manipulated variable is calculated based on fuzzy inference, non-linear and variable control gain, which could not be provided in the conventional P (Proportional), I (Integral) and D (Differential) control, can be realized, enabling highly precise control. Therefore, fuzzy control is widely applied to various and many control systems.
In order to realize satisfactory fuzzy control, it is necessary to construct fuzzy knowledge suitable for a specific plant.
Various constructions providing optimal fuzzy knowledge by tuning fuzzy control rules depending on the given plant have been proposed. One example of such a fuzzy control unit having the automatic tuning function is disclosed in "A design of self-tuning fuzzy controller", M. Maeda et al, Proceedings of 5th Fuzzy System Symposium, June 1989, pp. 89 to 94.
In the fuzzy control unit of Maeda et al, control deviation, primary difference (derivative) of the control deviation and secondary difference of the control deviation are used as variables (input variables) of an antecedent portion, and a primary difference of manipulated variable is used as a consequent variable. Each of the three antecedent variables is divided into three portions labeled with fuzzy labels N (Negative), Z (Zero) and P (Positive). The consequent variable is divided into seven portions, and a membership function corresponding to each fuzzy label is represented by one real number.
In the above described fuzzy control unit of Maeda et al., tuning of the fuzzy control rule is executed in two stages.
In the first stage, scaling factors are adjusted for normalizing range of input/output value of the control unit within [-1, 1]. Control response is made approaching to a target response by adjusting the scaling factors. While the scaling factors are adjusted, deviations between target values and desired values of overshoot, reaching time and amplitude are used as input variables, utilizing the fact that the fuzzy control unit is similar to a PID control unit, and the amount of correcting the scaling factors are found by simplified fuzzy inference.
In the second stage, values of the consequent portion of the fuzzy control rules (the membership function of the consequent portion is represented by one real number for simplified fuzzy inference) are corrected in real time, that is, at the sample time when the control response is observed. By this correction of rules, the control response is made close to the target response.
In the above described automatic tuning of the fuzzy control rules, when the real number of the conclusion portion (consequent portion) is corrected basically, a response deviation, which is a difference between ideal response waveform and control response waveform at a time of sampling, and an amount of change between the present response deviation and a response deviation of a prescribed preceding sample time are used as input variables, the real number (membership function) of the consequent portion of the fuzzy control rules are corrected only by whether each of these variables is positive, negative or zero. However, a final amount of correction is provided by multiplying the basic amount of correction and a grade of each fuzzy control rule. Therefore, in this automatic tuning method, the consequent portion of the fuzzy control rules can not be corrected finely corresponding to the magnitude of the response deviation and/or amount of change of the response deviation.
Therefore, if an ideal response waveform is much different from an control response waveform based on the fuzzy control rules before correction, the convergence efficiency in tuning is not always satisfactory, and accordingly, a desired result of tuning may not be provided, or the tuning takes substantial time.
In order to correct fuzzy knowledge, it is necessary to construct an initial fuzzy knowledge base beforehand, as starting rules for the correcting operation. Conventionally, a designer has constructed such knowledge base by dividing input variables appropriately for generating suitable fuzzy labels and taking in consideration initial values of the membership functions and the fuzzy rules. However, it is very difficult to create totally a new fuzzy knowledge base where no knowledge on the plant is acquired.
If improper initial values (rules), which cause a large difference between an ideal response waveform and an control response waveform based on the fuzzy control rules, are set as the initial fuzzy knowledge, sufficient convergence of the control action can not be ensured, and it may be impossible to obtain good control action. If such an improper initial fuzzy knowledge base is set, a desired tuning effect can not be provided.
In the above mentioned prior art article, similarity between a fuzzy control unit and a PID control unit is utilized to correct the scaling factor.
In a PID control unit, a manipulated variable is calculated by effecting proportional action (P), integral operation (I) and derivative action (D) based on the control response. Parameters (PID parameters) used in the PID action are, in most cases, set based on experience and/or intuition of an expert. When setting of PID parameters are carried out manually, an operator tunes the PID parameters every time the process is raised up (when it is initially activated) and when process characteristic is changed. This causes differences in control states due to variations in tuning, and the task of tuning itself becomes a burden.
In view of the foregoing, an automatic tuning unit for automatically tuning PID parameters to appropriate values according to the control response has been proposed, as disclosed in, for example, Japanese Patent Laying-Open No. 1-258003.
The prior art automatic tuning unit includes a first determining portion determining a PID parameter taking substantial account of a transient response characteristic; a second determining portion determining the PID parameter taking substantially account of an attenuation (dumping) response characteristic; a deciding unit deciding whether a control deviation is derived from a change of a set value or derived from an external disturbance or a process characteristic change; and a selecting circuit for selecting either the PID parameter from the first determining portion or the PID parameter from the second determining portion in accordance with the decision of the deciding unit.
The first and second determining portions tune the PID parameters, independently. When the control deviation is derived from a change of a set value, the PID parameter is tuned taking account of the transient response characteristic by the first determining portion. When the control deviation is derived from a transient response or a change in process characteristic, the PID parameter is tuned taking account of the attenuation characteristic by the second determining portion. Tuning of the PID parameters is effected by fuzzy inference.
In this case, knowledge bases corresponding to respective methods of tuning, that is, a knowledge base for carrying out tuning taking account of the transient response characteristic, and a knowledge base to carry out tuning taking account of the attenuation characteristic are necessary.
In the PID control, target values of evaluation reference of control (hereafter referred to as control target values) such as 2% of overshoot amount, 5% of amplitude attenuation ratio (dumping ratio) are set in advance, separately and independently from the set values. The knowledge bases for tuning are formed to attain the control target values.
Therefore, when a different plant is to be controlled, or when, even if the plant is the same, the process characteristic is changed, the PID parameters can not be tuned in accordance with the changed control target value by using the tuning knowledge base formed for the previously set control target value.
In order to tune the PID parameters according to different control target values, tuning knowledge bases must be provided corresponding to respective control target values. Generally, plural values are set for one control target value (for example, an amount of overshoot). Therefore, the number of necessary tuning knowledge bases is the number of combinations of various control target values.
However, it is very difficult to prepare tuning knowledge bases corresponding to all combinations of control target values and it requires much time and labor.
Even if such tuning knowledge bases are formed, there must be a memory device having a very large capacity to store the tuning knowledge bases, which increases the scale of the device as well as the cost thereof.